Scheduled television programming can be recorded at the time of broadcast when initially distributed for viewing, and can then be made available for on-demand viewing when requested by users of a television content distribution system. This is commonly referred to as Network Digital Video Recording (nDVR) and a viewer can request the recorded television content, such as movies and television programs, when convenient for the viewer. In this type of television content distribution system, the specific day and time that television content is broadcast is no longer the primary criteria for sorting the recorded television content, such as in a program guide (also commonly referred to as an electronic program guide or “EPG”). Because the recorded television content can be requested for viewing at any time that is convenient for a viewer, the grid structure of a program guide that indicates the day, time, and television channel for the selection and viewing of television content is almost irrelevant since users are not actually viewing the content when it is originally broadcast.
Having such a large selection of on-demand television content available, such as movies and television programs, can make it difficult for a viewer to locate something to watch that may be of interest to the viewer. The recorded television programs and movies that are available from an nDVR system are typically sorted alphabetically by title or by genre which can make it difficult for a viewer to find new viewing options that may be of interest to the viewer. In this type of system, a viewer can likely find a movie or television program if the title is already known. However, a viewer may never find unknown television content that would also be of interest to the viewer.
Collaborative filtering attempts to use the characteristics of other people to help determine what someone similar may be interested in watching. The primary problem with collaborative filtering is being able to associate a group of people from which to base movie and other television program recommendations. Traditional techniques for collaborative filtering use characteristics of the people in a group, such as age, gender, race, and/or location to create the groups. However, these traditional techniques rely on a presumption that people having some similar characteristics also share similar interests in movies and television program viewing choices. Thus, these traditional techniques associate people into groups, yet the people may not have common interests or even any basis from which to determine a likelihood of interest in the same movies and program viewing choices.